Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Journal of Mathematical and Fundamental Sciences Sinkron : Jurnal dan Penelitian Teknik Informatika The Indonesian Journal of Computer Science
Arifyanto, Mochamad Ikbal
Unknown Affiliation
Astronomy Chemistry Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Mathematics Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Akurasi Metode Mesin Pembelajaran dalam Analisis Variabel Penting Faktor Risiko Sindrom Down Palit, Oscar Oleta; Dhenanta, Rafi Prayoga; Susanto, Agnes Indarwati; Syawly, Adzky Matla; Ivansyah, Atthar Luqman; Santika, Aditya Purwa; Arifyanto, Mochamad Ikbal; Muttaqien, Fahdzi
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4354

Abstract

This study aims to identify risk factors for Down syndrome using machine learning methods. Data were obtained from an epidemiological case-control study conducted at Special Needs Schools in the cities and regencies of Tangerang. Methods used include Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Naive Bayes, K-Means, Artificial Neural Network (ANN), and Multi-Layer Perceptron (MLP). The results indicate that maternal age, paternal age, and the time interval of parents' work before the child's birth are the most influential factors in the incidence of Down syndrome. The SVM method achieved the highest accuracy of 76% with data categorized into two groups and using important variables. In addition to SVM, Naive Bayes and Random Forest methods also demonstrated good performance for analyzing epidemiological data with case-control types.
Digital Transformation of Electricity Bill Collection: Predicting Delays Using Machine Learning Utami, Dyah Puspita Sari Nilam; Arifyanto, Mochamad Ikbal
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14340

Abstract

Delays in electricity bill payments pose a significant challenge for PLN in maintaining financial stability and delivering equitable service quality to the public. This study aims to develop a payment delay prediction system to assist PLN UP3 Makassar Utara in prioritizing invoice distribution to customers with a high likelihood of late payments. The Random Forest algorithm was chosen for its ability to handle complex data and produce reliable predictions. This research analyses historical electricity customer data from 2018 to 2023, encompassing 227,163 entries. The data is processed using validation techniques such as K-Fold Validation and Rolling Window Validation to ensure the accuracy and generalizability of the model. The study's findings demonstrate that an accurate payment delay prediction model can be developed using the Random Forest method, incorporating historical features such as lag features, moving averages, and seasonal variables. Additionally, the system prioritizes invoice delivery to high-risk customers based on risk scores derived from historical delay patterns. This system reduces payment arrears at PLN UP3 Makassar Utara through proactive measures such as early notifications, personalized reminders, or payment incentives to encourage timely payments. As a result, the study indicates that the system effectively enhances the efficiency of payment management and supports the company's financial stability. However, the research is limited by the use of data from a single region, the absence of external factors in the model, and the high computational requirements. For broader implementation, further research should include data from other regions, consider external factors, and optimize computational resource usage.
Variability Survey of Southern Open Clusters NGC 6134, NGC 6416, Collinder 394, and Alessi 10 Mandey, Denny; Ramadhan, Dhimaz Gilang; Yusuf, Muhammad; Perhati, Teduh; Ramadhan, Sahlan; Pramono, Theofilus Hobba; Anin, Chornelis J’Be; Premadi, Premana Wardayanti; Jaelani, Anton Timur; Jatmiko, Agus Triono Puri; Arifyanto, Mochamad Ikbal
Journal of Mathematical and Fundamental Sciences Vol. 57 No. 3 (2026): (In Progress)
Publisher : Directorate for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.math.fund.sci.2026.57.3.1

Abstract

We present the results of a brightness variability survey of four southern open clusters, namely NGC 6134, NGC 6416, Collinder 394, and Alessi 10, which were observed using three telescopes following a time series photometric scheme during the dry season in 2022. The purpose of these observations was to detect variability events and their types of variability in star members of these four clusters. Data analysis was performed using a differential photometry method to produce light curves. Several new variable star candidates were discovered in the observed clusters. More observational data are needed to examine the consistency of the variabilities, estimate relevant periodicity, and determine the type of variable star.
Co-Authors Anin, Chornelis J’Be Atthar Luqman Ivansyah Darma, Rendy Dhenanta, Rafi Prayoga Jaelani, Anton Timur Jatmiko, Agus Triono Puri Mandey, Denny Muhammad Yusuf Muttaqien, Fahdzi Palit, Oscar Oleta Perhati, Teduh Pramono, Theofilus Hobba Premadi, Premana Wardayanti Ramadhan, Dhimaz Gilang Ramadhan, Sahlan Santika, Aditya Purwa Susanto, Agnes Indarwati Syawly, Adzky Matla Utami, Dyah Puspita Sari Nilam